Complex and dynamic networks are also driving the rise of AI in networking, Frey said. He added that humans can no longer manually keep up with network demands and environments, but AI engines can.
Though individual networks have different needs, many engineers want similar capabilities when they integrate AI into their networks. Experts agree that some of the most popular AI capabilities network engineers and managers want include the following:
According to McGillicuddy's research, network optimization and automated troubleshooting are the most popular use cases for AI. However, network professionals still want to fix the problem manually.
"Automated troubleshooting [can] isolate the problem, analyze what the cause of it is and then fix it," McGillicuddy said. "Usually with the fixing part, people don't want the machine to do it on its own. They want machines to present the fix, then they approve it."
Many of these capabilities can help mitigate security risks and threats, and according to Frey, many networking professionals look at AI as a way for organizations to get better and smarter about security. Amy Larsen DeCarlo, principal analyst for security and data center services at GlobalData, agreed.
"Network managers are looking for the same things that a security professional would be looking for in the sense of being able to recognize the problem and better address it before it becomes an outage," DeCarlo said.
Outside of security functions, Frey also listed some alternate use cases for AI, including documentation and change recommendations. Though less popular than other features, Frey explained that recommendations become some of the most valuable capabilities for network teams. But no matter the capability, every tool has a place in a network environment if it can fit the needs of the network and network team.
"I don't know if you can put one [capability] ahead of the other," DeCarlo said. "It depends on what tools they're using and how effective those tools have been."
Despite being a more recent development, GenAI has quickly made itself useful within the networking world. McGillicuddy said that in the past year and a half, network professionals have started using GenAI tools and found them useful. One of the most popular tools is also the most well-known: ChatGPT.
"One guy told me that doing stuff with ChatGPT could cut a task from four hours to 10 minutes in some cases," McGillicuddy said. However, he also warned that network professionals should be knowledgeable about the tools they're using, as GenAI tools can easily make mistakes.
"It's very possible it's going to do something wrong, hallucinate, and if you just have absolute faith in the type of content something like ChatGPT will create, you could mess up the network really badly," he said.
Aside from ChatGPT, vendors are also creating GenAI interfaces for their products, such as virtual assistants. McGillicuddy's research found that the most popular use cases for vendor GenAI products are the following:
DeCarlo also said that an additional benefit of GenAI tools is their training capabilities. Because of the tools' faster speeds and ability to go deeper into the network, she said they can offer potentially valuable insight into the network, and therefore expedite the learning process.
Regardless of origin or use, Frey credited GenAI's overall popularity with its ability to outperform older systems because of those systems' lack of sophistication. However, GenAI's complicated infrastructure, high speeds and performance sensitivity have also helped give rise to AIOps tools, which are needed to support it.
"We're not going to be able to manage these GenAI infrastructures without the help of AI tools, because humans won't be able to keep up with the change," Frey said.